code: def se(i, filters_se, filters_out, scope=None):
      x = reduce_mean(i, [1, 2], keepdims=True, name="squeeze")
      x = conv2d(
          x,
          filters_se,
          1,
          activation_fn=_swish,
          biases_initializer=tf.zeros_initializer(),
          scope="reduce",
      )
      x = conv2d(
          x,
          filters_out,
          1,
          activation_fn=tf.sigmoid,
          biases_initializer=tf.zeros_initializer(),
          scope="expand",
      )
      x = multiply(i, x, name="excite")
      return x
  
imports: from __future__ import absolute_import
  from __future__ import division
  import math
  import tensorflow
  from .layers import batch_norm
  from .layers import conv2d
  from .layers import convbn
  from .layers import convbnswish
  from .layers import dropout
  from .layers import fc
  from .layers import sconv2d
  from .layers import sconvbnswish
  from .ops import *
  from .ops import _swish
  from .utils import pad_info
  from .utils import set_args
  from .utils import var_scope
label_desc: Use image node_id 4 for calling a global function with example usage: se(i, filters_se, filters_out, scope) and returns: x